Quantmod - a Quantitative Financial Modelling & Trading Framework - can be used to load data from a variety of sources, including:
Quantmod enables 3 major types of column extraction functions. If you’re looking for the open, the high, or the series low - it’s available:
Op, Hi,Lo, Cl,Vo, Ad - extract the columns Open, High, Low, Close, Volume, and Adjusted (Yahoo)
is.OHLC, has.OHLC, has.Op, has.Cl, has.Hi, has.Lo, has.Ad, and has.Vo - checks the desired parameters
seriesHi and seriesLo - the highest and the lowest point in the series
Quantmod also provides a tool to visualize financial time series - the function chartSeries - with which we can create line charts, as well as OHLC bar and candle charts.
library(quantmod)Loading required package: xtsLoading required package: zoo
Attaching package: 'zoo'The following objects are masked from 'package:base':
as.Date, as.Date.numericLoading required package: TTRRegistered S3 method overwritten by 'quantmod':
method from
as.zoo.data.frame zoo We want to collect information about Apple. We only need to know their ticker, which is ‘AAPL’ on the ‘NASDAQ’.
We can get the required data with the getSymbols function:
apple_df <- getSymbols('AAPL', src='yahoo', auto.assign=FALSE)The auto.assign=FALSE parameter allows us to control the name of the dataframe and not have automatically put into memory
The src='yahoo' parameter allows us to specific the source of the stock information that we would like to use. We can
chartSeries(apple_df, name="AAPL stocks", subset="last 10 years", theme=chartTheme("white"))
barChart(apple_df, name="AAPL stocks", subset="last 5 years")
candleChart(apple_df, name="AAPL stocks", subset="last 3 months", multi.col=TRUE,theme="white") 
Non-OHLC and Volume series are handled automatically
getSymbols("AAPL")[1] "AAPL"is.OHLC(AAPL) # does the data contain at least OHL and C[1] TRUEhas.Vo(AAPL) # how about volume?[1] TRUEOp(AAPL) # select just the Open column AAPL.Open
2007-01-03 3.081786
2007-01-04 3.001786
2007-01-05 3.063214
2007-01-08 3.070000
2007-01-09 3.087500
2007-01-10 3.383929
2007-01-11 3.426429
2007-01-12 3.378214
2007-01-16 3.417143
2007-01-17 3.484286
...
2024-10-07 224.500000
2024-10-08 224.300003
2024-10-09 225.229996
2024-10-10 227.779999
2024-10-11 229.300003
2024-10-14 228.699997
2024-10-15 233.610001
2024-10-16 231.600006
2024-10-17 233.429993
2024-10-18 236.179993seriesHi(AAPL) # where and what was the high point AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume AAPL.Adjusted
2024-10-15 233.61 237.49 232.37 233.85 64751400 233.85HiCl(AAPL) #the percent change from high to close HiCl.AAPL
2007-01-03 -0.0321091481
2007-01-04 -0.0033739764
2007-01-05 -0.0133409869
2007-01-08 -0.0122500588
2007-01-09 -0.0044095800
2007-01-10 -0.0081798054
2007-01-11 -0.0101260827
2007-01-12 -0.0046285509
2007-01-16 -0.0015423833
2007-01-17 -0.0271516478
...
2024-10-07 -0.0177234257
2024-10-08 -0.0009292480
2024-10-09 -0.0009140662
2024-10-10 -0.0020043866
2024-10-11 -0.0081077572
2024-10-14 -0.0018555762
2024-10-15 -0.0153269582
2024-10-16 -0.0014647439
2024-10-17 -0.0072696693
2024-10-18 -0.0049961585
AAPL[‘2024’] #returns all Apple’s 2024 OHLC
AAPL[‘2024-01’] #now just January of 2024
AAPL[’2024-06::2024-08-15 #Jun of 24through Aug 15 of 24
AAPL[‘::’] # everything in AAPL
AAPL[‘2022::’] # everything in AAPL, from 2022 onward
non.contiguous <- c(‘2007-01’,‘2007-02’,‘2007-12’)
AAPL[non.contiguous] # different dates
AAPL['2023::'] AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume AAPL.Adjusted
2023-01-03 130.28 130.90 124.17 125.07 112117500 123.9046
2023-01-04 126.89 128.66 125.08 126.36 89113600 125.1826
2023-01-05 127.13 127.77 124.76 125.02 80962700 123.8551
2023-01-06 126.01 130.29 124.89 129.62 87754700 128.4122
2023-01-09 130.47 133.41 129.89 130.15 70790800 128.9373
2023-01-10 130.26 131.26 128.12 130.73 63896200 129.5119
2023-01-11 131.25 133.51 130.46 133.49 69458900 132.2462
2023-01-12 133.88 134.26 131.44 133.41 71379600 132.1669
2023-01-13 132.03 134.92 131.66 134.76 57809700 133.5043
2023-01-17 134.83 137.29 134.13 135.94 63646600 134.6734
...
2024-10-07 224.50 225.69 221.33 221.69 39505400 221.6900
2024-10-08 224.30 225.98 223.25 225.77 31855700 225.7700
2024-10-09 225.23 229.75 224.83 229.54 33591100 229.5400
2024-10-10 227.78 229.50 227.17 229.04 28183500 229.0400
2024-10-11 229.30 229.41 227.34 227.55 31759200 227.5500
2024-10-14 228.70 231.73 228.60 231.30 39882100 231.3000
2024-10-15 233.61 237.49 232.37 233.85 64751400 233.8500
2024-10-16 231.60 232.12 229.84 231.78 34082200 231.7800
2024-10-17 233.43 233.85 230.52 232.15 32993800 232.1500
2024-10-18 236.18 236.18 234.01 235.00 46416300 235.0000to.weekly()
to.monthly()
to.quarterly()
to.yearly()
to.yearly (apple_df) apple_df.Open apple_df.High apple_df.Low apple_df.Close
2007-12-31 3.081786 7.248571 2.925000 7.074286
2008-12-31 7.116786 7.152143 2.826429 3.048214
2009-12-31 3.067143 7.641071 2.792857 7.526071
2010-12-31 7.622500 11.666429 6.794643 11.520000
2011-12-30 11.630000 15.239286 11.089286 14.464286
2012-12-31 14.621429 25.181070 14.607143 19.006071
2013-12-31 19.779285 20.540714 13.753571 20.036428
2014-12-31 19.845715 29.937500 17.626785 27.594999
2015-12-31 27.847500 33.634998 23.000000 26.315001
2016-12-30 25.652500 29.672501 22.367500 28.955000
2017-12-29 28.950001 44.299999 28.690001 42.307499
2018-12-31 42.540001 58.367500 36.647499 39.435001
2019-12-31 38.722500 73.492500 35.500000 73.412498
2020-12-31 74.059998 138.789993 53.152500 132.690002
2021-12-31 133.520004 182.130005 116.209999 177.570007
2022-12-30 177.830002 182.940002 125.870003 129.929993
2023-12-29 130.279999 199.619995 124.169998 192.529999
2024-10-18 187.149994 237.490005 164.080002 235.000000
apple_df.Volume apple_df.Adjusted
2007-12-31 246995985600 5.974059
2008-12-31 285981206000 2.574141
2009-12-31 143253686800 6.355580
2010-12-31 151024927200 9.728352
2011-12-30 124059339600 12.214731
2012-12-31 131964204400 16.192688
2013-12-31 102421569600 17.499369
2014-12-31 63657952400 24.608042
2015-12-31 52264199600 23.866425
2016-12-30 38729911200 26.845057
2017-12-29 27243106000 39.855339
2018-12-31 34156144800 37.707047
2019-12-31 28254942800 71.250435
2020-12-31 39863855600 129.894363
2021-12-31 22812206100 174.900360
2022-12-30 22065504500 128.719345
2023-12-29 14804257200 191.802170
2024-10-18 12146251000 235.000000We can add technical analysis using tools from TTR package to the above chart. The library was developed by Josh Ulrich - details are available on CRAN
chartSeries(apple_df, subset="last 3 months")
addMACD()
addBBands() 
We use data from Oanda - please note that Oanda only provides historical data for the past 180 days.
gold_prices_recent<- getSymbols("XAU/USD",src="oanda") Gold prices, weekly for the last 3 months, with custom color candles using the quantmod function to.weekly.
chartSeries(to.weekly(XAUUSD),subset="last 3 months", up.col='white',dn.col='blue')
chartSeries(XAUUSD,name="Gold prices (.oz) in $USD")
We download S&P 500 data since 1927 until today.
sp500_df <- getSymbols('^GSPC', src='yahoo', from = "1927-01-01", to = "2024-10-24", auto.assign=FALSE)sp500_df <- getSymbols('^GSPC', src='yahoo', from = "1927-01-01", to = "2024-10-24", auto.assign=FALSE)chartSeries(sp500_df, name="S&P 500 since 1927", subset="last 98 years", theme=chartTheme("white"))
chartSeries(sp500_df, name="S&P 500", subset="last 6 months", theme=chartTheme("white"))
addMACD()
addBBands() 
write.csv(sp500_df, "sp500_1.csv", row.names = T)
Dataframe preview in RStudio:
The dates in the data frame are not imported correctly with this method - they are turned into ascending numbers (1,2,3,…). This is because quantmod::getSymbols() returns an xts object by default.
xts objects are based on zoo objects, which are a matrix with an ‘index’ attribute. The date we see when xts/zoo objects are printed in R is the index attribute, not row names (or record names).
The easiest way to fix it is ti write xts/zoo objects to a text file is with write.zoo(). It will automatically include the index in the first column of the file.
write.zoo(sp500_df, "sp500zoo.csv", sep = ",")The function summary() calculates the summary statistics for the financial data:
summary(apple_df) Index AAPL.Open AAPL.High AAPL.Low
Min. :2007-01-03 Min. : 2.835 Min. : 2.929 Min. : 2.793
1st Qu.:2011-06-13 1st Qu.: 12.624 1st Qu.: 12.682 1st Qu.: 12.481
Median :2015-11-23 Median : 28.006 Median : 28.260 Median : 27.668
Mean :2015-11-25 Mean : 56.915 Mean : 57.520 Mean : 56.334
3rd Qu.:2020-05-07 3rd Qu.: 79.657 3rd Qu.: 80.438 3rd Qu.: 79.119
Max. :2024-10-18 Max. :236.480 Max. :237.490 Max. :234.010
AAPL.Close AAPL.Volume AAPL.Adjusted
Min. : 2.793 Min. :2.405e+07 Min. : 2.358
1st Qu.: 12.585 1st Qu.:9.513e+07 1st Qu.: 10.628
Median : 28.017 Median :1.878e+08 Median : 25.457
Mean : 56.953 Mean :3.441e+08 Mean : 55.060
3rd Qu.: 79.729 3rd Qu.:4.642e+08 3rd Qu.: 77.592
Max. :235.000 Max. :3.373e+09 Max. :235.000 setSymbolLookup(
AAPL = 'yahoo',
GOOGL = 'yahoo',
TSLA = 'yahoo',
META = 'yahoo',
AMZN = 'yahoo',
NVDA = 'yahoo',
MSFT = 'yahoo',
"^GSPC" = 'yahoo',
"GC=F" = 'yahoo',
"BTC / USD" = 'yahoo'
)With the symbol lookup set, we don’t have to specify the source of data in the following operations.